prhegde
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t5-query-reformulation-RL

Text2Text Generation

text-generation-inference

Inference Endpoints

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prhegde commited on Apr 24

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@@ -22,6 +22,9 @@ tags:
 ## Model Summary
 This is a generative model designed specifically for search query rewriting, employing a sequence-to-sequence architecture for generating reformulated queries. It leverages a Reinforcement Learning framework to further boost performance, integrating a policy gradient algorithm. The model is trained with reward functions aimed at diversifying the generated queries by paraphrasing keywords. It can be integrated with sparse retrieval methods, such as bm25-based retrieval, to enhance document recall in search.
 ### Model Description
 Training Procedure

 ## Model Summary
 This is a generative model designed specifically for search query rewriting, employing a sequence-to-sequence architecture for generating reformulated queries. It leverages a Reinforcement Learning framework to further boost performance, integrating a policy gradient algorithm. The model is trained with reward functions aimed at diversifying the generated queries by paraphrasing keywords. It can be integrated with sparse retrieval methods, such as bm25-based retrieval, to enhance document recall in search.
+### Intended use cases
+Query rewriting for search (web, e-commerce), Virtual assistants and chatbots, Information retrieval
 ### Model Description
 Training Procedure